code: def normal(
      random_state,
      loc=0.0,
      scale=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw random samples from a normal (Gaussian) distribution.
  
      The probability density function of the normal distribution, first
      derived by De Moivre and 200 years later by both Gauss and Laplace
      independently [2]_, is often called the bell curve because of
      its characteristic shape (see the example below).
  
      The normal distributions occurs often in nature.  For example, it
      describes the commonly occurring distribution of samples influenced
      by a large number of tiny, random disturbances, each with its own
      unique distribution [2]_.
  
      Parameters
      ----------
      loc : float or array_like of floats
          Mean ("centre") of the distribution.
      scale : float or array_like of floats
          Standard deviation (spread or "width") of the distribution.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``loc`` and ``scale`` are both scalars.
          Otherwise, ``mt.broadcast(loc, scale).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized normal distribution.
  
      See Also
      --------
      scipy.stats.norm : probability density function, distribution or
          cumulative density function, etc.
  
      Notes
      -----
      The probability density for the Gaussian distribution is
  
      .. math:: p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
                       e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },
  
      where :math:`\mu` is the mean and :math:`\sigma` the standard
      deviation. The square of the standard deviation, :math:`\sigma^2`,
      is called the variance.
  
      The function has its peak at the mean, and its "spread" increases with
      the standard deviation (the function reaches 0.607 times its maximum at
      :math:`x + \sigma` and :math:`x - \sigma` [2]_).  This implies that
      `numpy.random.normal` is more likely to return samples lying close to
      the mean, rather than those far away.
  
      References
      ----------
      .. [1] Wikipedia, "Normal distribution",
             http://en.wikipedia.org/wiki/Normal_distribution
      .. [2] P. R. Peebles Jr., "Central Limit Theorem" in "Probability,
             Random Variables and Random Signal Principles", 4th ed., 2001,
             pp. 51, 51, 125.
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> mu, sigma = 0, 0.1 # mean and standard deviation
      >>> s = mt.random.normal(mu, sigma, 1000)
  
      Verify the mean and the variance:
  
      >>> (abs(mu - mt.mean(s)) < 0.01).execute()
      True
  
      >>> (abs(sigma - mt.std(s, ddof=1)) < 0.01).execute()
      True
  
      Display the histogram of the samples, along with
      the probability density function:
  
      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(s.execute(), 30, normed=True)
      >>> plt.plot(bins, (1/(sigma * mt.sqrt(2 * mt.pi)) *
      ...                mt.exp( - (bins - mu)**2 / (2 * sigma**2) )).execute(),
      ...          linewidth=2, color='r')
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .normal(handle_array(loc), handle_array(scale), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorNormal(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(loc, scale, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/normal.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: normal(random_state, loc, scale, size, chunk_size, gpu, dtype) and returns: op